Why this matters
Optimization time is wasted when the true bottleneck is unknown. This breakdown turns latency tuning into a measurable sequence.
What this tool does
Model end-to-end latency from mouse polling to displayed frame output and identify which pipeline segment dominates total delay.
Calculation model
Latency is composed from polling interval, frame cadence, queue depth, simulation/render stages, network path, and display response components.
Result interpretation
Use best/average/worst estimates as planning boundaries. Prioritize the largest contributors before optimizing smaller segments.
Limits and validation
This is a deterministic scenario model. Real measurements still depend on hardware, game engine, and network variability.
Use Cases
- Standardize your settings before ranked sessions and tournaments.
- Keep consistent aim behavior while switching between shooter titles.
- Document and share reproducible sensitivity settings with teammates.
Frequently Asked Questions
Is this a replacement for high-speed camera testing?
No. It is a planning model that helps prioritize likely bottlenecks before physical measurement.
Why are there best, average, and worst values?
Input and frame timing are phase-dependent. The range captures favorable, typical, and unfavorable alignment cases.
What should I optimize first?
Start with the largest latency component shown in the breakdown table, then re-run the scenario.
How does polling rate affect latency?
Higher polling rate reduces expected input sampling delay by shortening the report interval.
Does network always matter?
Network impact depends on game mode. Offline or local scenarios can set network contribution near zero.
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